In injection moulding the transfer characteristics of the conventional machine control to the process variables can vary by external influences and changed boundary conditions (Fig. 12.5). The conventional injection moulding machine control bases on machine variables. Thus, identical courses of machine variables lead to different process variables in different production cycles. These additional disturbances result in afluctuating part quality. To increase the process reproduc- ibility the concept of self-optimising injection moulding should compensate occurring process variations.
Fluctuating ambient temperature or varying material properties are systematic disturbances and can affect the product quality heavily. This includes the changes in the heat balance of the injection mould. Fluctuations in the heat balance of the mould occur for example by a non-identical repetitive process such as after changing machine parameters. Therefore, an autonomous parameter adaption has to com- pensatefluctuations, i.e. in the heat balance of the mould. In contrast to the machine variables process variables provide detailed information about the processes during the injection and holding pressure phase. The cavity pressure path over time for
Process values Machine values Quality values
Examples:
• Weight
• Shape dimensions
Examples:
• Cavity pressure
• Melt temperature
Examples:
• Screw movement
• Screw pressure
Material fluctuations Closing
behaviour of back flow valve
Temperature of mould and barrel
Accuracy of screw movement Ambient
conditions
Disturbances Categorization values
Fig. 12.5 Variables in injection moulding and typical disturbances
example correlates with various quality variables such as the part weight, the part precision, the warpage and the shrinkage, the morphology and sink marks.
Due to the presence of disturbances acting on the injection moulding process, an exclusive control of machine variables does not guarantee an ideal reproducibility of the process and thus constant part properties. Using the pvT-behaviour as a model to map process variables to quality variables, the course of cavity pressure can be adjusted to the actual path of melt temperature. Based on this context, the concept for the self-optimising injection moulding process is derived.
The pvT-behaviour represents the material based interactions between pressure and temperature in the mould of a plastic. It depicts the relationship between pressure, temperature and specific volume and thus allows a description of the link between the curves of cavity pressure, melt temperature and the resulting part properties in injection moulding.
The aim of the self-optimising injection moulding process is to ensure a constant quality of the moulded parts by realising an identical process course in the pvT-diagram (Fig. 12.6). The first requirement is to always achieve an identical, specified specific volume when reaching the 1-bar line (D) in every production cycle. This ensures a constant shrinkage in every cycle. Based on this requirement, the second requirement is to achieve an isochoric process course (C–D), which is characterised by the constant realisation of the given specific volume during the entire pressure phase. Due to the limits of machine the isobaric process control (B–C) is preceded the isochoric process control. Before, the injection and com- pression phase (A–B) is conventionally controlled by machine values.
At the Institute of Plastics Processing (IKV) a concept for a self-optimising injection moulding machine is being developed. The concept of self-optimising at injection moulding is divided in the MO-System using a model, which is based on the material behaviour, and ISA-Systems, which includes the determination of the melt temperature and a cavity pressure controller (Fig.12.7).
Based on the conventional injection moulding process the cavity pressure is measured by piezoelectric pressure sensors. The melt temperature is approximated based on the melt temperature in the screw and the mould temperature using the
A
B
C B‘
D E F
1 bar
Temperature
Specific Volume
v1bar vdem vamb
Tamb Tmelt
A - B: Injection and Compression B - C: Isobaric Process Control C - D: Isochoric Process Control D: Start of Isobaric Cooling E: Demoulding
F: Ambient Temperature reached
Fig. 12.6 pvT-Diagram as suitable model for the optimisation of holding-pressure phase
cooling calculation or directly measured by IR-Sensors (Menges et al.1980). After determination of the temperature and pressure in the cavity the working point and the optimised trajectory of the pressure can be calculated based on the material specific pvT-behaviour. A Model Predictive Controller (MPC) realises the pressure trajectory autonomously. Using the cavity pressure controller allows to compensate the pressure variations in the cavity. This reduces the natural process variations.
Furthermore, the adjustment of the cavity pressure trajectory to the measured temperature in the mould results in the compensation of temperaturefluctuations.
To simulate temperaturefluctuations the cooling units of the mould are turned off in an experiment after 15 cycles. The temperature path in the mould and the weight of the moulded part is observed using the conventional injection moulding process and the self-optimised concept (Fig.12.8). Compared to the conventional processing the weight reduction can massively be reduced by using the self-opti- mised processing concept.
To realise a pvT-optimised injection moulding process the user-friendly imple- mentation of a cavity pressure control is fundamental. The cooperation of the Institute of Plastics Processing (IKV) and the Institute of Automatic Control (IRT) focuses on the autonomous adaption of the cavity pressure control on boundary conditions to simplify the configuration of the cavity pressure controller. Therefore, a dynamic model for a MPC is developed for the injection moulding process (Fig.12.9).
ISA-System:
Information processing Sensor andActuator system MO-System:
Model-based Optimisation
system Working Point
Actuators MPC control of the
cavity pressure Signal Processing
Injection Moulding Process
pvT-Optimisation VolumenVspez
Model-Based Self-Optimisation (MBSO)
Machine level closed loop
Temperature and cavity pressure sensor
Disturbance Variables
Fig. 12.7 Transfer of the self-optimising concept to the injection moulding process
The model describes the correlation of the pressure in the screw (Ps) and the cavity pressure (Pcav). Therefore, the system is modelled with two vessels and a valve (Hopmann et al.2013). To adapt the physical motivated model to the time invariant measurements a time variant parameterisation of the valve is used.
The model is parameterised during an identification cycle. Therefore, a pro- duction cycle with a constant screw pressure is realised (Fig.12.10). Convention- ally the screw pressure is controlled in injection moulding. In the current configuration a simple PID-controller is used to realise the constant screw pressure.
The difference of the screw pressure to the cavity pressure is measured to detect the massflow between the vessels over the time. Based on the acquired data a char- acteristic map is created.
Q
M Xs Pcav
Ps
VM Vc Mcav,Vcav,
Tcav
Ms, Vs, Ts Pcav Ps
Time t / s
Pressure p / bar
screw pressure ps
cavity pressure pcav
Fig. 12.9 Dynamic model of the MPC to control the cavity pressure 45
55 65 75 85 95
0 10 20 30 40
Cavity wall temperature T / °C
Cycle c / -
Temperature conventional Temperature pvT-optimised
Distance
40 mm
Position of pressure sensor
58,6 59,0 59,4 59,8 60,2 60,6
0 10 20 30 40
Moulded part weight W / g
Cycle c / -
Weight conventional Weight pvT-optimised Turn off
cooling unit
Turn on cooling unit
Turn off cooling unit
Turn on cooling unit
Fig. 12.8 Compensating temperature fluctuations with self-optimising concept compared to conventional processing in injection moulding
Beforehand, the acquired data cannot be calculated and thus an easy parame- terisation is necessary. The advantages of identification process are varied. A con- stant screw pressure is feasible and can be incorporated into real-life workflow. The current concept of the self-optimisation injection moulding should be extended by cross-cycle optimisations to counteract disturbances such as viscosityfluctuations.
The combination of online control and cross-cycle optimisation is necessary to compensate the heat household fluctuations after changing machine parameters.
The compensation of the thermalfluctuations can be accomplished by the use of the previous concept of self-optimising injection moulding machine.